---
title: "geoCat"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: scroll
    social: menu
    source_code: embed
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)


library(data.table)
library(highcharter)
library(dplyr)
library(knitr)
library(maps)
data("county.fips")
data("uscountygeojson")
data("usgeojson")

#the COUNTYFP column is a character column, so we don't want to convert it to numeric
# df <- fread("../output/output_county_count.csv_combined.csv", colClasses = c("COUNTYFP" = "character"))
df <- fread("../output/output_state_count.csv_combined.csv", colClasses = c("COUNTYFP" = "character"))
test=df[which(df$STATE_NAME=="Wisconsin"),]
# order the data frame by the counts column
test= test[order(-test$counts),]

```

Column {data-width=650}
-----------------------------------------------------------------------


```{r echo=FALSE, message=FALSE, warning=FALSE}


# function to sum the counts column by a given column name
sum_by_column <- function(in_df, column_names) {
  #group by the list of column names and sum the counts column
  in_df <- in_df[, .(counts = sum(counts)), by = column_names]
  return(in_df)
}

```

### Count of Tracks by State
```{r}
sumByState <-
  sum_by_column(in_df = df, column_names = c("STATE_NAME"))
# log10 transform the counts column
sumByState$counts <- log10(sumByState$counts)
#create a chlorealpleth map of the US
highchart() %>%
  hc_add_series_map(
    usgeojson,
    sumByState,
    value = "counts",
    joinBy = c("name", "STATE_NAME"),
    name = "Count",
    dataLabels = list(enabled = TRUE, format = '{point.name}')
  ) %>%
  hc_mapNavigation(enabled = TRUE) %>%
  hc_colorAxis(stops = color_stops()) %>%
  hc_title(text = "Count of Tracks by State") %>%
  hc_add_theme(hc_theme_smpl()) %>%
  hc_legend(enabled = FALSE) %>%
  hc_tooltip(pointFormat = "{point.name}: {point.value}")

```

### Count of Tracks by County

polygons may not capture water like wisconsin apostle islands


```{r}
# TODO where is California counties
df$COUNTY_FIP=paste0(df$STATEFP,df$COUNTYFP)
sumByCounty <-
  sum_by_column(in_df = df, column_names = c("COUNTY_FIP"))
# log10 transform the counts column
sumByCounty$counts <- log10(sumByCounty$counts)
#create a chlorealpleth map of the US
highchart() %>%
  hc_add_series_map(
    uscountygeojson,
    sumByCounty,
    value = "counts",
    joinBy = c("fips", "COUNTY_FIP"),
    name = "Count") %>%
  hc_mapNavigation(enabled = TRUE) %>%
  hc_colorAxis(stops = color_stops()) %>%
  hc_title(text = "Count of Tracks by County") %>%
  hc_add_theme(hc_theme_smpl()) %>%
  hc_legend(enabled = FALSE) 
# %>%
#   hc_tooltip(pointFormat = "{point.name}: {point.value}")

```

### cat counts

```{r}
# map the Names "Rye8" "RyePhone" and "Rye13" to "RyeCat"
# create a vector of the names that need to be mapped
namesToMap <- c("Rye8", "RyePhone", "Rye13")
# replace the names in the Name column with "RyeCat"
# table(namesToMap %in% df$Name)
df$Name[df$Name %in% namesToMap] <- "RyeCat"

# get the names that start with sofia or that contain Papa or that start with tonga
tongaNames <- df$Name[grepl("^tonga", df$Name, ignore.case = TRUE)]
papaNames <- df$Name[grepl("papa", df$Name, ignore.case = TRUE)]
sofiaNames <- df$Name[grepl("^sofia", df$Name, ignore.case = TRUE)]
iacatNames <- c(tongaNames, papaNames, sofiaNames,"iha")
# map the iacatNames to iacat
df$Name[df$Name %in% iacatNames] <- "iacat"

# print the unique cat names that are not RyeCat or iacat
# unique(df$Name[!(df$Name %in% c("RyeCat", "iacat"))])

# subset the data frame to only include the rows where the Name column is equal to "RyeCat" or "iacat"
df <- df[Name %in% c("RyeCat", "iacat"), ]
 
# count wich cat wins which state
# the column Name contains the name of the cat
# the column counts contains the number of tracks
# create a color for the Name column with the most counts in each state

# get the max count for each state
maxByState <- df[, .(counts = max(counts)), by = c("STATE_NAME")]
# merge the max count back to the original data frame
df <- merge(df, maxByState, by = c("STATE_NAME"))
# filter the data frame to only include the rows where the counts column is equal to the max count
df <- df[counts.x == counts.y, ]
# create a color column that is equal to the name column
df$color <- df$Name
# create a color palette that maps RyeCat to bl
colorPalette <- c("#0000FF","#FF0000")
# create a named vector of colors
colorVector <- setNames(colorPalette, unique(df$Name))
# replace the color column with the color vector
df$color <- colorVector[df$color]
# log10 transform the counts column
df$counts <- log10(df$counts.x)
#create a chlorealpleth map of the US
highchart() %>%
  hc_add_series_map(
    usgeojson,
    df,
    value = "counts",
    joinBy = c("name", "STATE_NAME"),
    name = "Count",
    dataLabels = list(enabled = TRUE, format = '{point.name}')
  ) %>%
  hc_mapNavigation(enabled = TRUE) %>%
  hc_colorAxis(stops = color_stops()) %>%
  hc_title(text = "Which Cat Wins Which State") %>%
  hc_add_theme(hc_theme_smpl()) %>%
  hc_legend(enabled = FALSE) %>%
  hc_tooltip(pointFormat = "{point.name} is won by {point.Name}: {point.value}")

```

### cat counts

```{r}
# bar chart of number of states won by each cat
# count the number of states won by each cat
countByCat <- df[, .(counts = .N), by = c("Name")]
# create a bar chart of the number of states won by each cat
highchart() %>%
  hc_add_series(
    countByCat,
    type = "bar",
    hcaes(x = Name, y = counts),
    name = "Count"
  ) %>%
  hc_title(text = "Number of States Won by Each Cat") %>%
  hc_add_theme(hc_theme_smpl()) %>%
  hc_legend(enabled = FALSE) %>%
  hc_tooltip(pointFormat = "{point.Name} won {point.counts} states")